Hidden flows and waste processing--an analysis of illustrative futures

Forecasting municipal solid waste generation using artificial intelligence modelling approaches

Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg.

Evaluation of the environmental impact of Brownfield remediation options: comparison of two life cycle assessment-based evaluation tools

The choice between different options for the remediation of a contaminated site traditionally relies on economical, technical and regulatory criteria without consideration of the environmental impact of the soil remediation process itself. In the present study, the environmental impact assessment of two potential soil remediation techniques (excavation and off-site cleaning and in situ steam extraction) was performed using two life cycle assessment (LCA)-based evaluation tools, namely the REC (risk reduction, environmental merit and cost) method and the ReCiPe method. The comparison and evaluation of the different tools used to estimate the environmental impact of Brownfield remediation was based on a case study which consisted of the remediation of a former oil and fat processing plant. For the environmental impact assessment, both the REC and ReCiPe methods result in a single score for the environmental impact of the soil remediation process and allow the same conclusion to be drawn: excavation and off-site cleaning has a more pronounced environmental impact than in situ soil remediation by means of steam extraction. The ReCiPe method takes into account more impact categories, but is also more complex to work with and needs more input data. Within the routine evaluation of soil remediation alternatives, a detailed LCA evaluation will often be too time consuming and costly and the estimation of the environmental impact with the REC method will in most cases be sufficient. The case study worked out in this paper wants to provide a basis for a more sounded selection of soil remediation technologies based on a more detailed assessment of the secondary impact of soil remediation.

The 'bankability' of the new waste technologies: an econometric method for risk sharing in private finance waste contracts

The identification of risk and its appropriate allocation to partners in project consortia is essential for minimizing overall project risks, ensuring timely delivery and maximizing benefit for money invested. Risk management guidance available from government bodies, especially in the UK, does not specify methodologies for quantitative risk assessment, nor does it offer a procedure for allocating risk among project partners. Here, a methodology to quantify project risk and potential approaches to allocating risk and their implications are discussed. Construction and operation of a waste management facility through a public-private finance contract are discussed. Public-private partnership contracts are special purpose vehicle (SPV) financing methods promoted by the UK government to boost private sector investment in facilities for public service enhancement. Our findings question the appropriateness of using standard deviation as a measure for project risk and confirm the concept of portfolio theory, suggesting the pooling of risk can reduce total risk and its impact.